Many converged networks are bandwidth constrained relative to the volume of data they are required to handle. As a result, network users are very often not able to get the performance that they seek. These performance shortcomings manifest as data loss and increased latency of data transfer, i.e. poor QoS.
In many cases a subset of network users requires preferential treatment relative to others. Such users would like to incur lower data loss and lower data latency than others. In such cases the network data load needs to be partitioned into multiple traffic classes for the purpose of providing higher levels of QoS for preferred users.
Converged networks are composed of heterogeneous multiple nodes, with each node being a user of the network (edge), an intermediary between end points (router) or a combination of both. It is possible for a network management solution to reduce data loss and data latency for preferred users by monitoring and controlling the overall network data volume at network routers and at the network edge. However, very often, networks may be ‘opaque’ in the sense that it may not be possible for a network management solution to have visibility into the various network segments and thus fully control or monitor either the routers or the edges.
The characteristics of the load (data) generated by network users can be broadly classified as having (a) well defined characteristics (in terms of mathematical predictability) with fairly long durations (aka long-lived flows) or (b) highly stochastic (e.g., bursty traffic) with relatively short durations (aka short-lived flows). Thus traffic of type (a) is easy to predict and control, while traffic of type (b) presents significant challenges in terms of prediction and control.
Others have attempted to solve variants of this problem in the past. One such approach uses “time-delay” measurements to assess the characteristics of opaque networks. The approach is based on active probing of the network.
Measurement based admission control (MBAC) is another approach that has been considered. MBAC schemes use measurements to characterize the current data load. Others have shown that different MBAC algorithms all achieve almost identical levels of performance.
An approach for performing data load admission control in conjunction with data load accounting at the edge of the network is discussed in A. Poylisher, F. Anjum, L. Kant, R. Chadha, “QoS Mechanisms for Opaque MANETs”, MILCOM 2006. The article describes a method and system for providing QoS over opaque networks. Specifically, as traffic traverses between various user networks via the opaque network, gateways at the edge of the user networks keep a record of the packets traversing into the opaque network and packets traversing out of the opaque network. These gateways also know about the traffic classes that each of these packets belong to. The gateways at the ingress (the user network where the packets originate) and the gateways at the egress (the user network where the packets terminate) coordinate amongst themselves to exchange information about the number and latency of packets exchanged between the two. This information is used by the gateway at the ingress user network to estimate the state of the opaque network. Admission of data load is based on this estimated state of the opaque network.
While a time-delay measurement solution is generally a good approach for certain types of networks, it suffers from the following severe drawbacks: (a) it is expensive in terms of the overheads introduced in order to derive latency estimates, and (b) it does not consider multiple service (traffic) classes. Hence such an approach, albeit good for the environments that it has been proposed for, cannot be used (nor extended without having to undergo major transformations) to solve the problems associated with such converged networks.
The MBAC approaches suffer due to the requirement of complete knowledge and control over the elements in the path of the data packets. In the case of opaque networks, such knowledge and control is not feasible.
While the approach described in Poylisher et al above addresses network opacity, it does not address the case of short lived, bursty data loads that are hard to predict and control. When applied to such loads, the described mechanism was found to be overly conservative in admitting data into the network resulting in poor performance for all traffic classes. This happened because all admission control decisions were performed deterministically based on the last observed network state.